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Articles 1 - 9 of 9
Full-Text Articles in OS and Networks
Deepis: Susceptibility Estimation On Social Networks, Wenwen Xia, Yuchen Li, Jun Wu, Shenghong Li
Deepis: Susceptibility Estimation On Social Networks, Wenwen Xia, Yuchen Li, Jun Wu, Shenghong Li
Research Collection School Of Computing and Information Systems
Influence diffusion estimation is a crucial problem in social network analysis. Most prior works mainly focus on predicting the total influence spread, i.e., the expected number of influenced nodes given an initial set of active nodes (aka. seeds). However, accurate estimation of susceptibility, i.e., the probability of being influenced for each individual, is more appealing and valuable in real-world applications. Previous methods generally adopt Monte Carlo simulation or heuristic rules to estimate the influence, resulting in high computational cost or unsatisfactory estimation error when these methods are used to estimate susceptibility. In this work, we propose to leverage graph neural …
Influence Detection And Spread Estimation In Social Networks, Madhura Kaple
Influence Detection And Spread Estimation In Social Networks, Madhura Kaple
Master's Projects
A social network is an online platform, where people communicate and share information with each other. Popular social network features, which make them di erent from traditional communication platforms, are: following a user, re-tweeting a post, liking and commenting on a post etc. Many companies use various social networking platforms extensively as a medium for marketing their products. A xed amount of budget is alloted by the companies to maximize the positive in uence of their product. Every social network consists of a set of users (people) with connections between them. Each user has the potential to extend its in …
Prediction Of Topic Volume On Twitter, Yiye Ruan, Hemant Purohit, David Fuhry, Srinivasan Parthasarathy, Amit P. Sheth
Prediction Of Topic Volume On Twitter, Yiye Ruan, Hemant Purohit, David Fuhry, Srinivasan Parthasarathy, Amit P. Sheth
Kno.e.sis Publications
We discuss an approach for predicting microscopic (individual) and macroscopic (collective) user behavioral patterns with respect to specific trending topics on Twitter. Going beyond previous efforts that have analyzed driving factors in whether and when a user will publish topic-relevant tweets, here we seek to predict the strength of content generation which allows more accurate understanding of Twitter users' behavior and more effective utilization of the online social network for diffusing information. Unlike traditional approaches, we consider multiple dimensions into one regression-based prediction framework covering network structure, user interaction, content characteristics and past activity. Experimental results on three large Twitter …
Trust Networks, Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Andrew Henson, Amit P. Sheth
Trust Networks, Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Andrew Henson, Amit P. Sheth
Kno.e.sis Publications
Trust relationships occur naturally in many diverse contexts such as collaborative systems, e-commerce, interpersonal interactions, social networks, semantic sensor web, etc. As collaborating agents providing content and services become increasingly removed from the agents that consume them, the issue of robust trust inference and update become critical. There is a need to find online substitutes for traditional (direct or face-to-face) cues to derive measures of trust, and create efficient and secure system for managing trust, to support decision-making. Unfortunately, there is neither a universal notion of trust that is applicable to all domains nor a clear explication of its semantics …
Trust Model For Semantic Sensor And Social Networks: A Preliminary Report, Pramod Anantharam, Cory Andrew Henson, Krishnaprasad Thirunarayan, Amit P. Sheth
Trust Model For Semantic Sensor And Social Networks: A Preliminary Report, Pramod Anantharam, Cory Andrew Henson, Krishnaprasad Thirunarayan, Amit P. Sheth
Kno.e.sis Publications
Trust is an amorphous concept that is becoming Increasingly important in many domains, such as P2P networks, E-commerce, social networks, and sensor networks. While we all have an intuitive notion of trust, the literature is scattered with a wide assortment of differing definitions and descriptions; often these descriptions are highly dependent on a single domain or application of interest. In addition, they often discuss orthogonal aspects of trust while continuing to use the general term “trust”. In order to make sense of the situation, we have developed an ontology of trust that integrates and relates its various aspects into a …
Some Trust Issues In Social Networks And Sensor Networks, Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Andrew Henson, Amit P. Sheth
Some Trust Issues In Social Networks And Sensor Networks, Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Andrew Henson, Amit P. Sheth
Kno.e.sis Publications
Trust and reputation are becoming increasingly important in diverse areas such as search, e-commerce, social media, semantic sensor networks, etc. We review past work and explore future research issues relevant to trust in social/sensor networks and interactions. We advocate a balanced, iterative approach to trust that marries both theory and practice. On the theoretical side, we investigate models of trust to analyze and specify the nature of trust and trust computation. On the practical side, we propose to uncover aspects that provide a basis for trust formation and techniques to extract trust information from concrete social/sensor networks and interactions. We …
Artist Ranking Through Analysis Of On-Line Community Comments, Julia Grace, Daniel Gruhl, Kevin Haas, Meenakshi Nagarajan, Christine Robson, Nachiketa Sahoo
Artist Ranking Through Analysis Of On-Line Community Comments, Julia Grace, Daniel Gruhl, Kevin Haas, Meenakshi Nagarajan, Christine Robson, Nachiketa Sahoo
Kno.e.sis Publications
We describe an approach to measure the popularity of music tracks, albums and artists by analyzing the comments of music listeners in social networking online communities such as MySpace. This measure of popularity appears to be more accurate than the traditional measure based on album sales figures, as demonstrated by our focus group study. We faced many challenges in our attempt to generate a popularity ranking from the user comments on social networking sites, e.g., broken English sentences, comment spam, etc. We discuss the steps we took to overcome these challenges and describe an end to end system for generating …
Monetizing User Activity On Social Networks, Meenakshi Nagarajan, Kamal Baid, Amit P. Sheth, Shaojun Wang
Monetizing User Activity On Social Networks, Meenakshi Nagarajan, Kamal Baid, Amit P. Sheth, Shaojun Wang
Kno.e.sis Publications
In this work, we investigate techniques to monitize user activity on public forums, marketplaces and groups on social network sites. Our approach involves (a) identifying the monetization potential of user posts and (b) eliminating o- topic content in monetizable posts to use the most relevant keywords for advertising. Our first user study involving 30 users and data from MySpace and Facebook, shows that 52% of ad impressions shown after using our system were more targeted compared to the 30% relevant impressions generated without using our system. A second smaller study suggests that profile ads that are based on user activity …
Semantic Convergence Of Wikipedia Articles, Christopher J. Thomas, Amit P. Sheth
Semantic Convergence Of Wikipedia Articles, Christopher J. Thomas, Amit P. Sheth
Kno.e.sis Publications
Social networking, distributed problem solving and human computation have gained high visibility. Wikipedia is a well established service that incorporates aspects of these three fields of research. For this reason it is a good object of study for determining quality of solutions in a social setting that is open, completely distributed, bottom up and not peer reviewed by certified experts. In particular, this paper aims at identifying semantic convergence of Wikipedia articles; the notion that the content of an article stays stable regardless of continuing edits. This could lead to an automatic recommendation of good article tags but also add …